Deployments Software Engineer
Posted Updated
- The Deployments team is responsible for solving real world problems with our models and robots
- We tackle the full problem space: integrating with customer workflows, training models to solve their dexterous tasks, and ensuring the on-site reliability of the system
- This breadth of problem space is why we’re a full-stack robotics team - whether it’s thinking about customer facing experiences or fine-tuning models for tasks no robot has done before, we put forth the best solution Pi has to offer
- Design remote teleoperation interfaces: Develop comfortable and precise interfaces for controlling robots with many degrees of freedom
- Optimize for low-latency network performance: Engineer teleoperation interfaces that can continue functioning even in weak internet areas
- Optimize System Performance: Profile and optimize across compute, I/O, memory, scheduling, networking, and storage to meet real-time constraints and increase throughput
- Streaming & Video Systems: Develop and optimize real-time video streaming systems where frame timing and packet scheduling matter
- Reliability & Debugging: Build tooling for profiling, tracing, and debugging timing issues across distributed systems and hardware interfaces
- Cross-Functional Collaboration: Work with researchers, hardware engineers, and operations teams to integrate optimized pipelines into production workflows
- Strong programming skills in C++, Rust, or Python, with experience building and optimizing production software
- A mindset oriented around determinism, throughput, frame budgets, jitter minimization, and real-time correctness
- Ability to collaborate deeply with robot users, researchers, and platform engineers to build a precise and comfortable teleoperation interface
- Background in real-time or near–real-time systems, VR/AR, video pipelines, 3D engines, or streaming systems where latency budgets are strict
- Ability to optimize across the entire stack - drivers, networking, compute workloads, video frameworks, and distributed components
- Experience with profiling tools (perf, tracing, eBPF, GPU profilers, network analyzers) and comfort diving into complex performance issues
- Experience with robot teleoperation systems, VR/AR platforms, or low-latency 3D engines
- Camera system expertise (synchronization, capture pipelines, codecs, GPU offload)
- Streaming/video conferencing stack experience (WebRTC, real-time transport optimizations)
- Familiarity with distributed systems that process real-time data flows
- Background in robotics or autonomous systems (implementation, not research)